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Daniel Painter on how a single open-source release moves the hardware market: "Literally the week that DeepSeek V4 dropped, the market tightness of H100s went up over 200% in less than a day. And then for the next month or two, the rental rates for H100s increased about 1%... show more

MTS

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10,583 次观看 • 1 个月前 •via X (Twitter)

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Ben Horowitz on the two things every successful technology startup must do 1. Build a product that improves how some large group of people does something by 10x. “You have to build a transformational product. There is no way to build a great new company without a great product… If you look at the great technology companies, the thing that distinguishes them is the ability to keep coming up with a better way of doing things.” 2. Taking the market. “You can come up with the best product, but if you don’t [take the market], you really don’t have a company. Most technology businesses are network effects businesses. You have to win… If you’re an engineer, what business skills do you need to build a technology company? They’re all around the skills that you need to win the market. How do you out-market, out-sell, and beat the competition?… Do you build a good enough company that you go win?” Video source: UC Berkeley (2009)

Ben Horowitz on the two things every successful technology startup must do 1. Build a product that improves how some large group of people does something by 10x. “You have to build a transformational product. There is no way to build a great new company without a great product… If you look at the great technology companies, the thing that distinguishes them is the ability to keep coming up with a better way of doing things.” 2. Taking the market. “You can come up with the best product, but if you don’t [take the market], you really don’t have a company. Most technology businesses are network effects businesses. You have to win… If you’re an engineer, what business skills do you need to build a technology company? They’re all around the skills that you need to win the market. How do you out-market, out-sell, and beat the competition?… Do you build a good enough company that you go win?” Video source: UC Berkeley (2009)

Startup Archive

36,753 次观看 • 5 个月前

Ben Horowitz explains the two things every successful technology startup must do 1. Build a product that improves how some large group of people does something by 10x. “You have to build a transformational product. There is no way to build a great new company without a great product… If you look at the great technology companies, the thing that distinguishes them is the ability to keep coming up with a better way of doing things.” 2. Taking the market. “You can come up with the best product, but if you don’t [take the market], you really don’t have a company. Most technology businesses are network effects businesses. You have to win… If you’re an engineer, what business skills do you need to build a technology company? They’re all around the skills that you need to win the market. How do you out-market, out-sell, and beat the competition?… Do you build a good enough company that you go win?” Video Source: UC Berkeley

Ben Horowitz explains the two things every successful technology startup must do 1. Build a product that improves how some large group of people does something by 10x. “You have to build a transformational product. There is no way to build a great new company without a great product… If you look at the great technology companies, the thing that distinguishes them is the ability to keep coming up with a better way of doing things.” 2. Taking the market. “You can come up with the best product, but if you don’t [take the market], you really don’t have a company. Most technology businesses are network effects businesses. You have to win… If you’re an engineer, what business skills do you need to build a technology company? They’re all around the skills that you need to win the market. How do you out-market, out-sell, and beat the competition?… Do you build a good enough company that you go win?” Video Source: UC Berkeley

Startup Archive

77,268 次观看 • 2 年前

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

MTS

14,406 次观看 • 2 个月前

Apple just made every tech giant that went all in on AI look like clowns. For 12 months straight, Apple was the "biggest loser" of the AI era. Its AI team kept losing people. Its Siri overhaul kept getting delayed. And every headline said the same thing: Apple missed the biggest technology shift in a generation. But turns out, the OPPOSITE is actually the case... Apple passed Nvidia to briefly become the most valuable company on Earth again, worth around $4.88 trillion. Apple is up nearly 23% this year. Nvidia is up just 7.3%. Apple is now the best performer in the entire Mag 7. And when you look at why, it's almost funny. Apple won by REFUSING to spend the money everyone said it had to spend. Look at what the rest of Big Tech committed to the AI buildout this year: - Amazon, Google, Meta and Microsoft are spending more than $665 billion combined - Apple is spending about $13.5 billion - That is nearly 50x less than its rivals For a year, that gap was "proof" that Apple had fumbled it. Then the AI trade broke, and the company with no giant AI bill suddenly looked like the smartest one in the room. Apple never took on the risk. It never borrowed the billions to build data centers, and it never had to promise Wall Street that all that spending would pay off later. So when the trade cracked this week, Apple had nothing to crack. It still runs on iPhones and a services business that keeps setting records, not on a bet about AI revenue that has not shown up yet. And the crack itself was real: A Chinese startup called Moonshot dropped a new model that rivals the best from OpenAI and Anthropic, and it messed up the whole market in a single day. Investors are already calling it a Kimi moment, a rerun of the DeepSeek shock that hit these same stocks last year. The Philadelphia semiconductor index fell into a bear market, down 20% from its June peak. The Nasdaq 100 had its worst week in almost a month. Microsoft is now down 20% on the year, its worst stretch since 2022. Every company that went all in on the buildout got hit. Apple, the one that sat it out, is the company that came out on top. Why does this matter? Because for two years the entire market ran on one belief: Spend the most on AI or get left behind. The companies that spent $665 billion were called visionaries. The company that spent $13.5 billion was called a dinosaur. This week the market briefly went the other way. HSBC just upgraded Apple and lifted its price target to $366 from $260. Money that was chasing chips is now hiding in the one megacap with almost no exposure to the thing that just blew up. And the doubts are reaching the top now too: Societe Generale's head of US equity strategy warned this week that the biggest AI spenders are still burning cash so fast that investors are openly asking whether the spending ever pays off. What happens next: Nobody knows if this holds. Apple could lose the top spot again by Monday, and the AI bulls will tell you the buildout always looks reckless right before it pays off. But something bigger happened this week... For one day, the market stopped rewarding the biggest spender and started rewarding the one that kept its wallet shut. If that keeps happening, every board that bet the company on AI has a real problem. And the company that got mocked for doing the least became the safest place to hide from the trade it skipped. What do you think?

Apple just made every tech giant that went all in on AI look like clowns. For 12 months straight, Apple was the "biggest loser" of the AI era. Its AI team kept losing people. Its Siri overhaul kept getting delayed. And every headline said the same thing: Apple missed the biggest technology shift in a generation. But turns out, the OPPOSITE is actually the case... Apple passed Nvidia to briefly become the most valuable company on Earth again, worth around $4.88 trillion. Apple is up nearly 23% this year. Nvidia is up just 7.3%. Apple is now the best performer in the entire Mag 7. And when you look at why, it's almost funny. Apple won by REFUSING to spend the money everyone said it had to spend. Look at what the rest of Big Tech committed to the AI buildout this year: - Amazon, Google, Meta and Microsoft are spending more than $665 billion combined - Apple is spending about $13.5 billion - That is nearly 50x less than its rivals For a year, that gap was "proof" that Apple had fumbled it. Then the AI trade broke, and the company with no giant AI bill suddenly looked like the smartest one in the room. Apple never took on the risk. It never borrowed the billions to build data centers, and it never had to promise Wall Street that all that spending would pay off later. So when the trade cracked this week, Apple had nothing to crack. It still runs on iPhones and a services business that keeps setting records, not on a bet about AI revenue that has not shown up yet. And the crack itself was real: A Chinese startup called Moonshot dropped a new model that rivals the best from OpenAI and Anthropic, and it messed up the whole market in a single day. Investors are already calling it a Kimi moment, a rerun of the DeepSeek shock that hit these same stocks last year. The Philadelphia semiconductor index fell into a bear market, down 20% from its June peak. The Nasdaq 100 had its worst week in almost a month. Microsoft is now down 20% on the year, its worst stretch since 2022. Every company that went all in on the buildout got hit. Apple, the one that sat it out, is the company that came out on top. Why does this matter? Because for two years the entire market ran on one belief: Spend the most on AI or get left behind. The companies that spent $665 billion were called visionaries. The company that spent $13.5 billion was called a dinosaur. This week the market briefly went the other way. HSBC just upgraded Apple and lifted its price target to $366 from $260. Money that was chasing chips is now hiding in the one megacap with almost no exposure to the thing that just blew up. And the doubts are reaching the top now too: Societe Generale's head of US equity strategy warned this week that the biggest AI spenders are still burning cash so fast that investors are openly asking whether the spending ever pays off. What happens next: Nobody knows if this holds. Apple could lose the top spot again by Monday, and the AI bulls will tell you the buildout always looks reckless right before it pays off. But something bigger happened this week... For one day, the market stopped rewarding the biggest spender and started rewarding the one that kept its wallet shut. If that keeps happening, every board that bet the company on AI has a real problem. And the company that got mocked for doing the least became the safest place to hide from the trade it skipped. What do you think?

Ricardo

37,412 次观看 • 1 个月前

Brad Gerstner: Companies Will Pay 5x More for the Best AI, No Evidence of Pricing Pressure from Open Source Brad Gerstner: “Jason, you talked about summarizing a document, it may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model. But if you're talking about replacing a software engineer for two hours, that may take two million expensive tokens, and the consequence of using something that's 95% as good is really high. Because you have a long-running task, and if the task breaks early, or it breaks in the middle, or it breaks at the end, there's a huge cost to that.” @jason: “You still burn the tokens, right? And back to this analogy I was using, you're pulling the slot machine, and you lose.” Brad: “And (you lose) the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So three consulting firms, they're competing. They need the smartest consultant. They're charging $200 an hour. The difference between spending $3 on a cheap model or $15 on an expensive model to replace a $200/hour consultant, it's just irrelevant. That inference cost difference is irrelevant if you're getting something that's bulletproof for $15, and so I think that's what we're seeing play out. The best evidence for all of this is just revenue growth. I'm talking about, what is Anthropic's revenue growth compared to OpenAI, compared to the open source models? Millions of independent actors are choosing every single day. The open source companies are growing, right? But they're growing selling something that is really, really cheap. And there's room in every single market for premium products, for mid-tier products, and for commodity products, and I think we see a lot of this token growth, people are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won't pay for the frontier stuff. There is no evidence of that on the field today.”

Brad Gerstner: Companies Will Pay 5x More for the Best AI, No Evidence of Pricing Pressure from Open Source Brad Gerstner: “Jason, you talked about summarizing a document, it may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model. But if you're talking about replacing a software engineer for two hours, that may take two million expensive tokens, and the consequence of using something that's 95% as good is really high. Because you have a long-running task, and if the task breaks early, or it breaks in the middle, or it breaks at the end, there's a huge cost to that.” @jason: “You still burn the tokens, right? And back to this analogy I was using, you're pulling the slot machine, and you lose.” Brad: “And (you lose) the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So three consulting firms, they're competing. They need the smartest consultant. They're charging $200 an hour. The difference between spending $3 on a cheap model or $15 on an expensive model to replace a $200/hour consultant, it's just irrelevant. That inference cost difference is irrelevant if you're getting something that's bulletproof for $15, and so I think that's what we're seeing play out. The best evidence for all of this is just revenue growth. I'm talking about, what is Anthropic's revenue growth compared to OpenAI, compared to the open source models? Millions of independent actors are choosing every single day. The open source companies are growing, right? But they're growing selling something that is really, really cheap. And there's room in every single market for premium products, for mid-tier products, and for commodity products, and I think we see a lot of this token growth, people are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won't pay for the frontier stuff. There is no evidence of that on the field today.”

The All-In Podcast

52,527 次观看 • 1 个月前

Founder of the world's largest oil derivatives market maker (Onyx Capital Group) on what last week actually looked like from inside the market. "Bid-offer on Brent went from 1–2 cents to 50 cents minimum." "Price swings as you're hedging - dollars per barrel at a time." "20–30% up. Then 20–30% down. In a day." "That's enough to make your year, lose your year, and blow up. In one day." "People getting the tap on the shoulder literally Monday morning." "If you've been in the outright price, I'd be incredibly surprised if you made money."

Founder of the world's largest oil derivatives market maker (Onyx Capital Group) on what last week actually looked like from inside the market. "Bid-offer on Brent went from 1–2 cents to 50 cents minimum." "Price swings as you're hedging - dollars per barrel at a time." "20–30% up. Then 20–30% down. In a day." "That's enough to make your year, lose your year, and blow up. In one day." "People getting the tap on the shoulder literally Monday morning." "If you've been in the outright price, I'd be incredibly surprised if you made money."

Ethan Kho

62,591 次观看 • 5 个月前

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

Josh Caplan

40,039 次观看 • 1 年前

Elon Musk on what it means to work “super hard” “Depending on how well you want to do—particularly if you’re starting a company—you need to work super hard. What does ‘super hard’ mean? Well, when my brother and I were starting our first company, instead of getting an apartment we just rented a small office and slept on the couch. We showered at the YMCA. And we had just one computer so the website was up during the day and I was coding at night—seven days a week, all the time.” Elon recalls briefly having a girlfriend at the time who also had to sleep at the office if she wanted to be with him. And he frames the math of it as follows if you’re starting a company: “If somebody else is working 50 hours a week and you’re working 100, you’ll get twice as much done in the course of the year as the other company.” Video source: USC (2014)

Elon Musk on what it means to work “super hard” “Depending on how well you want to do—particularly if you’re starting a company—you need to work super hard. What does ‘super hard’ mean? Well, when my brother and I were starting our first company, instead of getting an apartment we just rented a small office and slept on the couch. We showered at the YMCA. And we had just one computer so the website was up during the day and I was coding at night—seven days a week, all the time.” Elon recalls briefly having a girlfriend at the time who also had to sleep at the office if she wanted to be with him. And he frames the math of it as follows if you’re starting a company: “If somebody else is working 50 hours a week and you’re working 100, you’ll get twice as much done in the course of the year as the other company.” Video source: USC (2014)

Startup Archive

69,014 次观看 • 1 年前

Elon Musk on what it means to work “super hard” “Depending on how well you want to do—particularly if you’re starting a company—you need to work super hard. What does ‘super hard’ mean? Well, when my brother and I were starting our first company, instead of getting an apartment we just rented a small office and slept on the couch. We showered at the YMCA. And we had just one computer so the website was up during the day and I was coding at night—seven days a week, all the time.” Elon recalls briefly having a girlfriend at the time who also had to sleep at the office if she wanted to be with him. And he frames the math of it as follows if you’re starting a company: “If somebody else is working 50 hours a week and you’re working 100, you’ll get twice as much done in the course of the year as the other company.” Video source: USC (2014)

Elon Musk on what it means to work “super hard” “Depending on how well you want to do—particularly if you’re starting a company—you need to work super hard. What does ‘super hard’ mean? Well, when my brother and I were starting our first company, instead of getting an apartment we just rented a small office and slept on the couch. We showered at the YMCA. And we had just one computer so the website was up during the day and I was coding at night—seven days a week, all the time.” Elon recalls briefly having a girlfriend at the time who also had to sleep at the office if she wanted to be with him. And he frames the math of it as follows if you’re starting a company: “If somebody else is working 50 hours a week and you’re working 100, you’ll get twice as much done in the course of the year as the other company.” Video source: USC (2014)

Startup Archive

131,219 次观看 • 8 个月前

The most amazing aspect of DeepSeek Open Source is that just the Reasoning Engine can be isolated and used with any other LLM. In fact you can have a mixture of Reasoning Engines cascade around a problem and then use an Operator type agent AI to function on the the results.

The most amazing aspect of DeepSeek Open Source is that just the Reasoning Engine can be isolated and used with any other LLM. In fact you can have a mixture of Reasoning Engines cascade around a problem and then use an Operator type agent AI to function on the the results.

Brian Roemmele

398,786 次观看 • 1 年前

Zuck: "For anyone who shows up now and is doing open source now that we have done it, there's a question. They already have their revealed preference for what they would build if open source didn't exist - it wasn't open source. We need to be careful about relying on their continued behavior for the future of the technology that we're gonna build at this company."

Zuck: "For anyone who shows up now and is doing open source now that we have done it, there's a question. They already have their revealed preference for what they would build if open source didn't exist - it wasn't open source. We need to be careful about relying on their continued behavior for the future of the technology that we're gonna build at this company."

Dwarkesh Patel

288,685 次观看 • 1 年前

“There was never any talk of the risk.” Peter Schiff is just lying at this point. The risk of $STRC was always obvious: it is preferred equity issued by a Bitcoin Treasury Company. Saylor said it clearly: if Bitcoin goes down long term or forever, that is the risk. And what are we seeing now? Bitcoin under pressure. Liquidity rotated into AI and IPOs. Maximum fear. STRC down. That is not some hidden scandal. That is risk premium repricing. Strategy does not magically control STRC’s market price. They can adjust the dividend rate, but the market prices the risk of Bitcoin and Strategy’s ability to keep paying dividends. And when Bitcoin starts going up again, what do you think happens? The same risk premium reprices in the other direction. If you buy preferred equity in a Bitcoin company, you are making one core assumption: Bitcoin goes up over the long term. That was never hidden.

“There was never any talk of the risk.” Peter Schiff is just lying at this point. The risk of $STRC was always obvious: it is preferred equity issued by a Bitcoin Treasury Company. Saylor said it clearly: if Bitcoin goes down long term or forever, that is the risk. And what are we seeing now? Bitcoin under pressure. Liquidity rotated into AI and IPOs. Maximum fear. STRC down. That is not some hidden scandal. That is risk premium repricing. Strategy does not magically control STRC’s market price. They can adjust the dividend rate, but the market prices the risk of Bitcoin and Strategy’s ability to keep paying dividends. And when Bitcoin starts going up again, what do you think happens? The same risk premium reprices in the other direction. If you buy preferred equity in a Bitcoin company, you are making one core assumption: Bitcoin goes up over the long term. That was never hidden.

Alex 👽

21,220 次观看 • 2 个月前

AMD CEO LISA SU JUST DOUBLED HER OWN CPU FORECAST IN 6 MONTHS In November, she told investors the CPU market would grow 18-20% per year through 2030. This morning she revised that number: "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." The driver: agents. Six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center. The new ratio is closer to 1 to 1. Su on CNBC: "Each of us could have 2 or 3 or even 5 or 10 agents doing work on our behalf. That means you need a lot more CPU compute."

AMD CEO LISA SU JUST DOUBLED HER OWN CPU FORECAST IN 6 MONTHS In November, she told investors the CPU market would grow 18-20% per year through 2030. This morning she revised that number: "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." The driver: agents. Six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center. The new ratio is closer to 1 to 1. Su on CNBC: "Each of us could have 2 or 3 or even 5 or 10 agents doing work on our behalf. That means you need a lot more CPU compute."

WOLF

88,438 次观看 • 3 个月前

OpenAI people alerted that you will send personal data to DeepSeek, but you can actually rent a GPU from Hyperbolic and host your own R1 model using ollama to avoid sending data to any company. DeepSeek R1 is the true user-owned AGI. The beauty of open source!

OpenAI people alerted that you will send personal data to DeepSeek, but you can actually rent a GPU from Hyperbolic and host your own R1 model using ollama to avoid sending data to any company. DeepSeek R1 is the true user-owned AGI. The beauty of open source!

Jasper

880,229 次观看 • 1 年前

ELON: OPENAI WAS MEANT TO BE NONPROFIT AND OPEN SOURCE He’s explaining the original intent and where it went off course. “I came up with the name OpenAI as an open source and nonprofit organization. I funded OpenAI for the first roughly fifty million dollars. It was intended to be a nonprofit, open source company. Now they’re trying to change that for their own financial benefit into a for profit company that is closed source.” Source: Astro Greek

ELON: OPENAI WAS MEANT TO BE NONPROFIT AND OPEN SOURCE He’s explaining the original intent and where it went off course. “I came up with the name OpenAI as an open source and nonprofit organization. I funded OpenAI for the first roughly fifty million dollars. It was intended to be a nonprofit, open source company. Now they’re trying to change that for their own financial benefit into a for profit company that is closed source.” Source: Astro Greek

Mario Nawfal

40,859 次观看 • 7 个月前

Qullamaggie on Situational Awareness “I guess it was an okay setup, but it failed in a big way. That was near the peak of the SPAC market. You can see why it failed, because NASDAQ pulled back. Nasdaq had a big run, multi-month run. You wanna buy breakouts when the market comes out of a multi-month sideways consolidation or a multi-month sell-off. Here, good time to buy breakouts. Here, not so much. And that's why it's failed. The setup itself was okay, but once the market is straight up for multiple months, you gotta be careful buying breakouts. The setups themselves were okay. But the market started pulling back mid-February and everything failed. When is the best time to buy breakouts? Chat, come on. Come on, chat. When is the best time to buy breakouts? What do we want to see in the moving averages? After correction? Yeah, but the moving averages. When the 10-day is above the 20-day, yeah, more. Rising 10-day, 20-day. We want to see the 10-day above the 20-day, and both the 10-day and 20-day need to be rising. Or one of them can be sideways, but at least one of them needs to be rising. That's when you have good conditions, generally, for breakouts. In late February, the 10-day had started sloping down, the 20-day was sideways. So that was a warning sign. That you shouldn't buy breakouts. NIO, big move, pulled back to the 10-day, started building a flag and it went straight up. Why did it go straight up? Well, because the market had just come out of a multi-month correction. And that was the start of a multi-month move. And NIO had shown strength all the way. That's a good time to buy breakouts, when the market comes out of a multi-month sideways or multi-month correction. Not when the market has already run for two, three months straight up, and it's starting to lose momentum. Not a good time to buy breakouts.”

Qullamaggie on Situational Awareness “I guess it was an okay setup, but it failed in a big way. That was near the peak of the SPAC market. You can see why it failed, because NASDAQ pulled back. Nasdaq had a big run, multi-month run. You wanna buy breakouts when the market comes out of a multi-month sideways consolidation or a multi-month sell-off. Here, good time to buy breakouts. Here, not so much. And that's why it's failed. The setup itself was okay, but once the market is straight up for multiple months, you gotta be careful buying breakouts. The setups themselves were okay. But the market started pulling back mid-February and everything failed. When is the best time to buy breakouts? Chat, come on. Come on, chat. When is the best time to buy breakouts? What do we want to see in the moving averages? After correction? Yeah, but the moving averages. When the 10-day is above the 20-day, yeah, more. Rising 10-day, 20-day. We want to see the 10-day above the 20-day, and both the 10-day and 20-day need to be rising. Or one of them can be sideways, but at least one of them needs to be rising. That's when you have good conditions, generally, for breakouts. In late February, the 10-day had started sloping down, the 20-day was sideways. So that was a warning sign. That you shouldn't buy breakouts. NIO, big move, pulled back to the 10-day, started building a flag and it went straight up. Why did it go straight up? Well, because the market had just come out of a multi-month correction. And that was the start of a multi-month move. And NIO had shown strength all the way. That's a good time to buy breakouts, when the market comes out of a multi-month sideways or multi-month correction. Not when the market has already run for two, three months straight up, and it's starting to lose momentum. Not a good time to buy breakouts.”

Lone

11,870 次观看 • 1 年前

Marc Andreessen on the 3 things he looks for when investing in a startup The first thing Marc Andreesen looks for is a big market: “Is there a big existing market that you think you can go after and displace incumbents? Or do you believe there will be a new market that will be big?” The second thing he looks for is a 10x better product: “Is there a fundamental technology or economic change that justifies a new company? And the way I always think about that is: Is there a 10x change happening in the technology landscape? Is something 10x faster, 10x cheaper, or 10x better? If it’s not 10x, we as both VCs and entrepreneurs have to ask ourselves if it’s really worth doing because it’s really hard to start new companies . . . Existing companies are usually pretty good at what they do. So for a new company to exist, it has to bring a product to market that’s so much better than what exists that it punches through the status quo.” The third is the team: “Is the team outstanding? . . . You want to have a founding team of complementary skillsets. You want to have at least one super strong technologist — quite possibly more than one. Some of the best startups are actually more than one founding technologist. And then it often helps to have someone who is a marketing or salesperson who has a really good understanding of business.” Marc believes that you need all three of these, but if you’re going to compromise on one of those as an investor, it should be the product: “A great market is a lot easier to make up for with iterative product execution. The problem with a poor or small market is that even if you do a good job on the product, there just aren’t that many customers so it’s hard to ever get big and people get demoralized . . . And then we evaluate the team of a startup by its ability to get into a big market with a good product.”

Marc Andreessen on the 3 things he looks for when investing in a startup The first thing Marc Andreesen looks for is a big market: “Is there a big existing market that you think you can go after and displace incumbents? Or do you believe there will be a new market that will be big?” The second thing he looks for is a 10x better product: “Is there a fundamental technology or economic change that justifies a new company? And the way I always think about that is: Is there a 10x change happening in the technology landscape? Is something 10x faster, 10x cheaper, or 10x better? If it’s not 10x, we as both VCs and entrepreneurs have to ask ourselves if it’s really worth doing because it’s really hard to start new companies . . . Existing companies are usually pretty good at what they do. So for a new company to exist, it has to bring a product to market that’s so much better than what exists that it punches through the status quo.” The third is the team: “Is the team outstanding? . . . You want to have a founding team of complementary skillsets. You want to have at least one super strong technologist — quite possibly more than one. Some of the best startups are actually more than one founding technologist. And then it often helps to have someone who is a marketing or salesperson who has a really good understanding of business.” Marc believes that you need all three of these, but if you’re going to compromise on one of those as an investor, it should be the product: “A great market is a lot easier to make up for with iterative product execution. The problem with a poor or small market is that even if you do a good job on the product, there just aren’t that many customers so it’s hard to ever get big and people get demoralized . . . And then we evaluate the team of a startup by its ability to get into a big market with a good product.”

Startup Archive

17,333 次观看 • 6 个月前

Wow… this is amazing This latest episode of the The All-In Podcast is a complete referendum of closed source AI (ie. Anthropic/OAI) in a way I haven’t yet heard from industry leaders Open source AI is about not just have a moment. It’s about to be a monsoon. A flood of demand. Watch the clip here from david friedberg. There’s a huge vibe shift underway. The move away from proprietary AI that captures companies IP (Ant/OAI), to Open source AI that is sovereign and owned by enterprises This is the future that Satya, Karp, and Jensen have been speaking about. I expect a tidal wave of demand for open source AI to be on our doorstep after the 4th of July festivities are over. GPU rental rates are about to soar once again Incredibly bullish for $NVDA $PLTR and $MSFT : all key partners in enabling this future

Wow… this is amazing This latest episode of the The All-In Podcast is a complete referendum of closed source AI (ie. Anthropic/OAI) in a way I haven’t yet heard from industry leaders Open source AI is about not just have a moment. It’s about to be a monsoon. A flood of demand. Watch the clip here from david friedberg. There’s a huge vibe shift underway. The move away from proprietary AI that captures companies IP (Ant/OAI), to Open source AI that is sovereign and owned by enterprises This is the future that Satya, Karp, and Jensen have been speaking about. I expect a tidal wave of demand for open source AI to be on our doorstep after the 4th of July festivities are over. GPU rental rates are about to soar once again Incredibly bullish for $NVDA $PLTR and $MSFT : all key partners in enabling this future

Nick Dorsey

102,535 次观看 • 1 个月前